The aim of this article is to promote the development of rural finance and the further informatization of rural banks. Based on DL (deep learning) and artificial intelligence technology, data pre-processing and feature selection are conducted on the customer information of rural banks in a certain region, including the historical deposit and loan, transaction record, and credit information. Besides, four DL models are proposed with a precision of more than 87% by test to improve the simulation effect and explore the application of DL. The BLSTM-CNN (Bi-directional Long Short-Term Memory-Convolutional Neural Network) model with a precision of 95.8%, which integrates RNN (Recurrent Neural Network) and CNN (Convolutional Neural Network) in parallel, solves the shortcomings of RNN and CNN separately. The research result can provide a more reasonable prediction model for rural banks, and ideas for the development of rural informatization and promoting rural governance.
PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.
In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are analysed. With the help of rough set theory, the financial risk indicators are selected, and the financial risk early warning index system is established. The financial risk early warning model is constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, the accuracy and feasibility of the constructed neural network model are verified. The results show that rough set theory can be used to screen financial risk indicators and select important indicators, which can simplify the data and reduce the complexity of calculation. BPNN can calculate the simplified data and identify and evaluate the financial risk. Empirical analysis shows that the proposed method can shorten the training time of the model to a certain extent, and improve the accuracy of financial risk prediction.
This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users’ behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model’s accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.
The identification and classification of professional terms of machine translation are studied in this work, to improve the accuracy and professionalism of computer aided translation (CAT) software. Firstly, the current situation and related fields of machine translation are analyzed to summarize the difficulties and shortcomings in machine translation. Secondly, the concept of term is introduced to conduct targeted research on the imbalance problem of terminology classification and recognition in machine translation. Thirdly, a term recognition model based on integrated recognition method is proposed. Finally, the classification accuracy and recall rate of the model are verified using the method of confusion matrix in experiments. The results demonstrate that in comparison of the recall rate, classification accuracy, and f value in different fields, the classification accuracy of network terms by the hybrid method combining the over-sampling method and under-sampling method is the highest of 77%, that of sports terms is the lowest of 71%, and that of economic terms is 74%. Among the recall rate, accuracy rate and f value, the recall rate is the highest, reaching more than 80%, especially for economic terms of 91%. The combination of over-sampling and under-sampling performs better than the under-sampling with playback and under-sampling without playback in terms of term recognition and classification in different fields. Through the classification results before and after integration, it is obvious that the integration of each base classifier not only effectively improves the classification accuracy of terms, but also greatly improves the recall rate. This term recognition model can help CAT software in improving the recognition accuracy of term translation, which has certain practical effects and provides reference for research in related fields.
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